AtacR
Toshiyuki Sakai
9/5/2019
AtacR
Normalication
Using GoF(Goodness of fit)
Differential expression
Mutants_Treatments vs Wildtype_Untreated
Read Dataset
Use AtacR package.
Set gene regions as windows.
library(atacr)
library(magrittr)
library(UpSetR)
library(SummarizedExperiment)## Loading required package: GenomicRanges## Loading required package: stats4## Loading required package: BiocGenerics## Loading required package: parallel##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:parallel':
##
## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
## parLapplyLB, parRapply, parSapply, parSapplyLB## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
##
## anyDuplicated, append, as.data.frame, basename, cbind,
## colMeans, colnames, colSums, dirname, do.call, duplicated,
## eval, evalq, Filter, Find, get, grep, grepl, intersect,
## is.unsorted, lapply, lengths, Map, mapply, match, mget, order,
## paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind,
## Reduce, rowMeans, rownames, rowSums, sapply, setdiff, sort,
## table, tapply, union, unique, unsplit, which, which.max,
## which.min## Loading required package: S4Vectors##
## Attaching package: 'S4Vectors'## The following object is masked from 'package:base':
##
## expand.grid## Loading required package: IRanges## Loading required package: GenomeInfoDb## Loading required package: Biobase## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.## Loading required package: DelayedArray## Loading required package: matrixStats##
## Attaching package: 'matrixStats'## The following objects are masked from 'package:Biobase':
##
## anyMissing, rowMedians## Loading required package: BiocParallel##
## Attaching package: 'DelayedArray'## The following objects are masked from 'package:matrixStats':
##
## colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges## The following objects are masked from 'package:base':
##
## aperm, apply# When use this script first time, we use make_counts method in AtacR package.
# After second time, we can use .rds files.
# test_AtacR_desc_final.csv, gene_symbol_region.gff made by XXXXX.py
if (file.exists("../data/rna_seq_count_data.rds") ){
data <- readRDS("../data/rna_seq_count_data.rds")
} else {
my_params = make_params(
paired_map = FALSE,
minq = 30,
dedup = TRUE
)
data <- make_counts('../data/gene_symbol_region.gff',
'../data/AtacR_desc.csv',
is_rnaseq = TRUE,
filter_params = my_params
)
}Save all dataset (.rds)
Save read counts data (.csv)
Fix some data
read counts in AT1G65483_wt_un_r1~3 = 0.
We modified AT1G65483_wt_un_r1 = 1 to calculate differential expression.
write.csv(assays(data$bait_windows)[[1]], file="../outputs/rna_seq_count_in_mRNA.csv")
saveRDS(data, file="../outputs/rna_seq_count_data.rds")
assays(data$bait_windows)[[1]]["AT1G65483","wt_un_r1"] <- 1Summary
summary(data)## ATAC-seq experiment of 34 treatments in 102 samples
## Treatments: eds1_a2,eds1_a4,eds1_kv,eds1_mk,gdna,gh_a2,gh_a4,gh_kv,gh_mk,myc234_a2,myc234_a4,myc234_kv,myc234_mk,peds_a2,peds_a4,peds_kv,peds_mk,r1ab_a2,r1ab_a4,r1ab_kv,r1ab_mk,sid2_a2,sid2_a4,sid2_kv,sid2_mk,tplr14_a2,tplr14_a4,tplr14_kv,tplr14_mk,wt_a2,wt_a4,wt_kv,wt_mk,wt_un
## Samples: eds1_a2_r1,eds1_a2_r2,eds1_a2_r3,eds1_a4_r1,eds1_a4_r2,eds1_a4_r3,eds1_kv_r1,eds1_kv_r2,eds1_kv_r3,eds1_mk_r1,eds1_mk_r2,eds1_mk_r3,gdna_r1,gdna_r2,gdna_r3,gh_a2_r1,gh_a2_r2,gh_a2_r3,gh_a4_r1,gh_a4_r2,gh_a4_r3,gh_kv_r1,gh_kv_r2,gh_kv_r3,gh_mk_r1,gh_mk_r2,gh_mk_r3,myc234_a2_r1,myc234_a2_r2,myc234_a2_r3,myc234_a4_r1,myc234_a4_r2,myc234_a4_r3,myc234_kv_r1,myc234_kv_r2,myc234_kv_r3,myc234_mk_r1,myc234_mk_r2,myc234_mk_r3,peds_a2_r1,peds_a2_r2,peds_a2_r3,peds_a4_r1,peds_a4_r2,peds_a4_r3,peds_kv_r1,peds_kv_r2,peds_kv_r3,peds_mk_r1,peds_mk_r2,peds_mk_r3,r1ab_a2_r1,r1ab_a2_r2,r1ab_a2_r3,r1ab_a4_r1,r1ab_a4_r2,r1ab_a4_r3,r1ab_kv_r1,r1ab_kv_r2,r1ab_kv_r3,r1ab_mk_r1,r1ab_mk_r2,r1ab_mk_r3,sid2_a2_r1,sid2_a2_r2,sid2_a2_r3,sid2_a4_r1,sid2_a4_r2,sid2_a4_r3,sid2_kv_r1,sid2_kv_r2,sid2_kv_r3,sid2_mk_r1,sid2_mk_r2,sid2_mk_r3,tplr14_a2_r1,tplr14_a2_r2,tplr14_a2_r3,tplr14_a4_r1,tplr14_a4_r2,tplr14_a4_r3,tplr14_kv_r1,tplr14_kv_r2,tplr14_kv_r3,tplr14_mk_r1,tplr14_mk_r2,tplr14_mk_r3,wt_a2_r1,wt_a2_r2,wt_a2_r3,wt_a4_r1,wt_a4_r2,wt_a4_r3,wt_kv_r1,wt_kv_r2,wt_kv_r3,wt_mk_r1,wt_mk_r2,wt_mk_r3,wt_un_r1,wt_un_r2,wt_un_r3
## Bait regions used: 52
## Total Windows: 104
##
## On/Off target read counts:
## sample off_target on_target percent_on_target
## 1 eds1_a2_r1 535272 6813408 92.71608
## 2 eds1_a2_r2 866734 4516921 83.90064
## 3 eds1_a2_r3 600574 4253966 87.62861
## 4 eds1_a4_r1 669760 2949519 81.49466
## 5 eds1_a4_r2 1046612 4034113 79.40034
## 6 eds1_a4_r3 718131 3460738 82.81518
## 7 eds1_kv_r1 466351 2524127 84.40547
## 8 eds1_kv_r2 713463 2998005 80.77680
## 9 eds1_kv_r3 394773 2242144 85.02899
## 10 eds1_mk_r1 344486 1294368 78.98007
## 11 eds1_mk_r2 653586 1310516 66.72342
## 12 eds1_mk_r3 416530 1028606 71.17711
## 13 gdna_r1 13402 6760 33.52842
## 14 gdna_r2 11575 6131 34.62668
## 15 gdna_r3 11497 5567 32.62424
## 16 gh_a2_r1 470469 2917555 86.11376
## 17 gh_a2_r2 297422 1490610 83.36596
## 18 gh_a2_r3 610761 3271003 84.26589
## 19 gh_a4_r1 661336 4029737 85.90224
## 20 gh_a4_r2 168012 837371 83.28876
## 21 gh_a4_r3 661863 3299518 83.29211
## 22 gh_kv_r1 854857 4545999 84.17182
## 23 gh_kv_r2 566501 3448229 85.88944
## 24 gh_kv_r3 516607 2864966 84.72288
## 25 gh_mk_r1 1476438 3878330 72.42760
## 26 gh_mk_r2 522528 1018137 66.08426
## 27 gh_mk_r3 473263 1849173 79.62213
## 28 myc234_a2_r1 891659 9019312 91.00331
## 29 myc234_a2_r2 1040718 7128121 87.25990
## 30 myc234_a2_r3 684428 6144520 89.97755
## 31 myc234_a4_r1 758354 7354204 90.65210
## 32 myc234_a4_r2 831423 7254296 89.71739
## 33 myc234_a4_r3 657413 7346057 91.78590
## 34 myc234_kv_r1 873898 7974571 90.12374
## 35 myc234_kv_r2 678928 4078510 85.72913
## 36 myc234_kv_r3 532061 5448421 91.10338
## 37 myc234_mk_r1 369879 1539403 80.62732
## 38 myc234_mk_r2 551731 1624700 74.64974
## 39 myc234_mk_r3 417546 1524779 78.50277
## 40 peds_a2_r1 591875 3321091 84.87401
## 41 peds_a2_r2 250049 1035827 80.55419
## 42 peds_a2_r3 675164 4637311 87.29097
## 43 peds_a4_r1 557807 3161051 85.00058
## 44 peds_a4_r2 13071 94154 87.80975
## 45 peds_a4_r3 904446 9411858 91.23285
## 46 peds_kv_r1 377514 1201677 76.09447
## 47 peds_kv_r2 214757 710449 76.78820
## 48 peds_kv_r3 638846 5678061 89.88673
## 49 peds_mk_r1 543897 703042 56.38143
## 50 peds_mk_r2 105051 122944 53.92399
## 51 peds_mk_r3 1010932 5345638 84.09627
## 52 r1ab_a2_r1 740094 8950078 92.36243
## 53 r1ab_a2_r2 617984 5115989 89.22241
## 54 r1ab_a2_r3 669800 5914292 89.82700
## 55 r1ab_a4_r1 946963 13205740 93.30896
## 56 r1ab_a4_r2 302827 2630425 89.67607
## 57 r1ab_a4_r3 621682 6120986 90.77988
## 58 r1ab_kv_r1 1725748 23677372 93.20655
## 59 r1ab_kv_r2 368466 3395624 90.21102
## 60 r1ab_kv_r3 653248 7297218 91.78353
## 61 r1ab_mk_r1 1760853 16448880 90.33015
## 62 r1ab_mk_r2 230443 565217 71.03750
## 63 r1ab_mk_r3 456801 2996391 86.77163
## 64 sid2_a2_r1 1026895 7012396 87.22655
## 65 sid2_a2_r2 149090 909752 85.91952
## 66 sid2_a2_r3 561296 3758063 87.00511
## 67 sid2_a4_r1 1319090 12404840 90.38839
## 68 sid2_a4_r2 388071 3363746 89.65645
## 69 sid2_a4_r3 613855 5050928 89.16366
## 70 sid2_kv_r1 741646 4826886 86.68148
## 71 sid2_kv_r2 270848 1585434 85.40911
## 72 sid2_kv_r3 535664 3214101 85.71473
## 73 sid2_mk_r1 897334 3244768 78.33627
## 74 sid2_mk_r2 241621 537169 68.97482
## 75 sid2_mk_r3 515073 2869223 84.78050
## 76 tplr14_a2_r1 577338 5078470 89.79212
## 77 tplr14_a2_r2 273345 2101935 88.49209
## 78 tplr14_a2_r3 457328 3495833 88.43133
## 79 tplr14_a4_r1 428528 4754136 91.73151
## 80 tplr14_a4_r2 250498 2532460 90.99886
## 81 tplr14_a4_r3 530529 4702373 89.86167
## 82 tplr14_kv_r1 631357 5575859 89.82866
## 83 tplr14_kv_r2 409717 3005163 88.00201
## 84 tplr14_kv_r3 361573 2728562 88.29912
## 85 tplr14_mk_r1 744305 3192639 81.09435
## 86 tplr14_mk_r2 215068 470466 68.62767
## 87 tplr14_mk_r3 465019 1744543 78.95425
## 88 wt_a2_r1 969966 10456691 91.51138
## 89 wt_a2_r2 466516 3820573 89.11812
## 90 wt_a2_r3 883420 7809293 89.83723
## 91 wt_a4_r1 1081963 14550301 93.07865
## 92 wt_a4_r2 616462 6609402 91.46867
## 93 wt_a4_r3 803346 9509319 92.21010
## 94 wt_kv_r1 872784 9129107 91.27381
## 95 wt_kv_r2 1864114 15055972 88.98283
## 96 wt_kv_r3 733621 8551487 92.09895
## 97 wt_mk_r1 522204 4827155 90.23801
## 98 wt_mk_r2 114902 475252 80.53017
## 99 wt_mk_r3 405915 3209662 88.77316
## 100 wt_un_r1 661253 1050531 61.37054
## 101 wt_un_r2 543072 928314 63.09113
## 102 wt_un_r3 474208 852907 64.26775
## Quantiles:
## $bait_windows
## eds1_a2_r1 eds1_a2_r2 eds1_a2_r3 eds1_a4_r1 eds1_a4_r2 eds1_a4_r3
## 1% 0.0 0.51 15.81 0.00 9.69 0.51
## 5% 105.3 52.95 138.35 82.05 89.85 36.60
## 95% 612209.6 305203.15 297397.85 179818.85 219240.25 215957.70
## 99% 843114.7 854820.71 729309.44 555801.17 899143.25 731027.56
## eds1_kv_r1 eds1_kv_r2 eds1_kv_r3 eds1_mk_r1 eds1_mk_r2 eds1_mk_r3
## 1% 37.23 0.0 15.30 0.00 0.00 0.00
## 5% 126.85 9.9 47.75 3.75 2.00 0.00
## 95% 144710.50 142104.9 138194.90 61212.70 72885.75 68214.05
## 99% 563908.65 827999.9 545970.66 474976.48 501729.18 397104.31
## gdna_r1 gdna_r2 gdna_r3 gh_a2_r1 gh_a2_r2 gh_a2_r3 gh_a4_r1
## 1% 15.02 7.55 14.57 0.00 0.0 0.0 13.26
## 5% 20.85 14.55 18.55 36.05 7.3 31.1 126.95
## 95% 359.65 325.00 315.90 252713.40 89983.6 316417.9 330185.15
## 99% 492.92 512.63 383.75 476478.52 395308.5 653637.2 720620.90
## gh_a4_r2 gh_a4_r3 gh_kv_r1 gh_kv_r2 gh_kv_r3 gh_mk_r1 gh_mk_r2
## 1% 0.00 0.51 12.75 3.57 0.0 0.51 0.00
## 5% 1.65 5.40 76.60 19.25 2.2 29.50 0.55
## 95% 41158.90 267568.00 509199.00 148366.70 294364.1 210106.80 39511.40
## 99% 210801.13 614184.02 797749.89 1059589.95 712977.5 1316424.96 361246.80
## gh_mk_r3 myc234_a2_r1 myc234_a2_r2 myc234_a2_r3 myc234_a4_r1
## 1% 0.00 0.00 8.16 47.03 63.75
## 5% 8.80 534.55 240.60 169.80 453.05
## 95% 76761.95 581865.30 519314.25 534043.25 550122.35
## 99% 718017.54 689453.12 968558.15 743397.39 685801.72
## myc234_a4_r2 myc234_a4_r3 myc234_kv_r1 myc234_kv_r2 myc234_kv_r3
## 1% 3.57 54.98 110.67 9.69 53.04
## 5% 294.85 298.60 660.55 58.65 143.20
## 95% 559558.55 720508.40 576098.45 248381.30 463115.55
## 99% 897005.56 930991.19 1000539.43 655530.75 794828.27
## myc234_mk_r1 myc234_mk_r2 myc234_mk_r3 peds_a2_r1 peds_a2_r2
## 1% 36.72 8.16 4.08 0.00 0.00
## 5% 84.40 38.60 103.15 50.85 6.60
## 95% 121817.95 126012.25 98873.10 216700.45 69010.15
## 99% 240896.10 193050.41 254003.44 707553.43 195668.66
## peds_a2_r3 peds_a4_r1 peds_a4_r2 peds_a4_r3 peds_kv_r1 peds_kv_r2
## 1% 45.90 0.0 0.00 26.52 0.0 0.0
## 5% 260.35 55.5 1.10 480.65 10.9 0.0
## 95% 315010.05 257966.0 7283.35 762021.80 84154.6 46584.5
## 99% 682274.99 418957.6 13839.57 1164510.93 298147.0 180324.2
## peds_kv_r3 peds_mk_r1 peds_mk_r2 peds_mk_r3 r1ab_a2_r1 r1ab_a2_r2
## 1% 77.01 0.0 0.51 130.56 26.52 0.0
## 5% 355.50 2.1 1.00 519.20 160.30 38.0
## 95% 443150.30 66865.8 6742.35 353323.65 667024.60 376864.5
## 99% 869471.12 178263.2 43415.26 880920.25 785596.47 808616.2
## r1ab_a2_r3 r1ab_a4_r1 r1ab_a4_r2 r1ab_a4_r3 r1ab_kv_r1 r1ab_kv_r2
## 1% 42.13 77.52 18.87 74.24 19.38 0.00
## 5% 197.20 171.55 86.40 159.95 138.60 48.95
## 95% 396556.45 1097876.45 198774.65 520283.40 1859368.45 258776.60
## 99% 862334.44 1459497.72 292363.30 795467.78 2982785.93 577330.66
## r1ab_kv_r3 r1ab_mk_r1 r1ab_mk_r2 r1ab_mk_r3 sid2_a2_r1 sid2_a2_r2
## 1% 42.33 62.73 3.06 5.10 18.36 2.04
## 5% 178.85 693.85 8.75 91.70 65.05 7.75
## 95% 642748.85 1626059.75 29827.40 99988.35 589915.80 63502.10
## 99% 1009939.63 3674497.75 152201.88 1125561.02 1040531.25 152777.70
## sid2_a2_r3 sid2_a4_r1 sid2_a4_r2 sid2_a4_r3 sid2_kv_r1 sid2_kv_r2
## 1% 12.24 24.99 87.7 1.53 26.01 11.65
## 5% 180.20 314.90 149.2 104.80 383.10 100.15
## 95% 302038.75 1187459.85 214217.6 500594.05 448638.15 98259.35
## 99% 657585.18 1776684.49 405487.2 702533.52 591645.58 275376.20
## sid2_kv_r3 sid2_mk_r1 sid2_mk_r2 sid2_mk_r3 tplr14_a2_r1 tplr14_a2_r2
## 1% 5.61 6.12 5.1 0.51 1.53 0.0
## 5% 39.30 83.75 32.5 68.45 93.00 26.2
## 95% 302743.70 218186.30 40361.5 110537.25 409228.95 168623.2
## 99% 717274.56 1012627.36 149245.2 986438.43 629053.84 261479.0
## tplr14_a2_r3 tplr14_a4_r1 tplr14_a4_r2 tplr14_a4_r3 tplr14_kv_r1
## 1% 16.32 5.61 0.0 19.38 19.12
## 5% 63.90 167.30 9.2 101.65 165.95
## 95% 295536.45 407674.70 204904.3 446603.15 447551.00
## 99% 511838.68 557252.34 322617.4 552011.88 701260.75
## tplr14_kv_r2 tplr14_kv_r3 tplr14_mk_r1 tplr14_mk_r2 tplr14_mk_r3
## 1% 1.02 4.59 3.06 0.00 0.0
## 5% 56.30 29.55 83.00 6.20 10.4
## 95% 191000.20 248468.45 180164.50 36203.85 78385.3
## 99% 422717.77 490080.11 951944.90 90826.81 595843.1
## wt_a2_r1 wt_a2_r2 wt_a2_r3 wt_a4_r1 wt_a4_r2 wt_a4_r3
## 1% 57.63 0.00 30.60 24.48 8.67 6.12
## 5% 192.20 64.05 184.85 173.35 88.70 301.95
## 95% 834809.40 284033.80 570219.05 1117991.45 526551.65 765873.65
## 99% 1580525.86 533019.13 1340331.73 1623420.93 770767.31 1161230.66
## wt_kv_r1 wt_kv_r2 wt_kv_r3 wt_mk_r1 wt_mk_r2 wt_mk_r3 wt_un_r1
## 1% 0.0 12.14 0.0 6.63 0.00 22.95 0.0
## 5% 79.2 185.10 218.6 99.20 23.10 109.65 0.0
## 95% 762052.9 860718.10 781883.7 244969.30 43908.95 116827.40 122538.8
## 99% 1472739.3 2590975.58 1199536.8 1436644.37 75654.47 1123431.46 277569.6
## wt_un_r2 wt_un_r3
## 1% 0.00 0.00
## 5% 1.00 6.60
## 95% 90682.75 80633.15
## 99% 276634.10 221397.79
##
## $non_bait_windows
## eds1_a2_r1 eds1_a2_r2 eds1_a2_r3 eds1_a4_r1 eds1_a4_r2 eds1_a4_r3
## 1% 75.85 181.91 84.09 128.46 174.82 90.78
## 5% 337.15 347.50 172.90 300.85 353.45 215.50
## 95% 39479.55 73094.45 60014.50 50001.05 74830.20 41980.25
## 99% 149848.60 261214.55 173619.72 218259.91 352583.38 250958.39
## eds1_kv_r1 eds1_kv_r2 eds1_kv_r3 eds1_mk_r1 eds1_mk_r2 eds1_mk_r3
## 1% 69.63 123.53 42.84 5.1 4.08 10.2
## 5% 178.10 249.90 128.10 64.1 67.40 52.9
## 95% 26956.60 28235.95 18229.85 14884.6 28204.20 32123.3
## 99% 157170.17 258094.65 138334.47 128765.4 257575.68 144656.9
## gdna_r1 gdna_r2 gdna_r3 gh_a2_r1 gh_a2_r2 gh_a2_r3 gh_a4_r1
## 1% 40.57 28.81 37.53 85.52 37.17 135.89 97.14
## 5% 50.95 47.75 49.10 200.65 103.50 186.55 205.50
## 95% 553.90 441.45 442.80 48130.10 24253.70 57085.25 64721.55
## 99% 632.16 560.00 556.93 123907.60 89917.67 179554.02 191573.82
## gh_a4_r2 gh_a4_r3 gh_kv_r1 gh_kv_r2 gh_kv_r3 gh_mk_r1 gh_mk_r2
## 1% 24.46 114.96 138.5 38.68 31.56 47.43 11.22
## 5% 111.00 213.60 262.2 156.95 124.30 224.65 68.75
## 95% 12126.75 60879.20 57253.2 48793.50 34784.15 78954.35 19462.75
## 99% 53081.10 191924.22 266905.1 166045.63 165205.31 560490.45 208251.29
## gh_mk_r3 myc234_a2_r1 myc234_a2_r2 myc234_a2_r3 myc234_a4_r1
## 1% 9.69 221.22 152.99 64.26 161.26
## 5% 84.70 410.95 373.45 220.15 260.15
## 95% 24383.50 59474.05 56964.30 52786.20 49169.60
## 99% 179575.36 193820.77 371400.64 210174.75 208288.86
## myc234_a4_r2 myc234_a4_r3 myc234_kv_r1 myc234_kv_r2 myc234_kv_r3
## 1% 94.1 59.33 98.93 72.64 61.0
## 5% 229.9 239.35 247.10 266.80 202.0
## 95% 55380.7 45738.60 68158.05 55318.90 48217.7
## 99% 270624.3 202089.23 271456.78 226746.18 154632.0
## myc234_mk_r1 myc234_mk_r2 myc234_mk_r3 peds_a2_r1 peds_a2_r2
## 1% 21.38 66.57 29.58 103.39 30.03
## 5% 141.75 165.20 125.25 271.35 97.00
## 95% 22500.40 19024.20 19373.70 50324.75 25081.70
## 99% 129510.60 217195.14 160125.26 161616.26 66202.89
## peds_a2_r3 peds_a4_r1 peds_a4_r2 peds_a4_r3 peds_kv_r1 peds_kv_r2
## 1% 79.52 136.23 2.02 105.78 46.29 33.11
## 5% 208.60 247.05 4.00 312.75 117.50 76.30
## 95% 64728.70 49427.60 1014.25 76708.55 21541.35 18431.55
## 99% 198295.83 139198.63 3022.12 268566.93 123756.65 63543.43
## peds_kv_r3 peds_mk_r1 peds_mk_r2 peds_mk_r3 r1ab_a2_r1 r1ab_a2_r2
## 1% 67.49 23.32 0.51 92.6 190.62 165.85
## 5% 221.20 71.70 15.30 289.9 375.95 358.05
## 95% 60105.95 44996.30 9410.70 42510.3 59803.60 47453.15
## 99% 183088.60 176518.26 32207.49 380197.6 178574.07 183014.99
## r1ab_a2_r3 r1ab_a4_r1 r1ab_a4_r2 r1ab_a4_r3 r1ab_kv_r1 r1ab_kv_r2
## 1% 76.10 92.63 39.56 73.74 285.8 39.25
## 5% 280.25 298.15 152.20 283.60 459.4 117.85
## 95% 56361.75 83352.85 25743.40 50921.75 153756.4 31681.20
## 99% 191934.06 263121.93 75008.68 180846.07 468283.6 107277.38
## r1ab_kv_r3 r1ab_mk_r1 r1ab_mk_r2 r1ab_mk_r3 sid2_a2_r1 sid2_a2_r2
## 1% 98.37 143.31 5.10 12.75 256.85 25.38
## 5% 275.35 735.35 39.40 59.20 478.10 72.55
## 95% 52871.85 79122.15 6821.00 23144.65 91130.95 12090.75
## 99% 187646.94 591357.94 94211.53 173121.68 268720.74 44991.93
## sid2_a2_r3 sid2_a4_r1 sid2_a4_r2 sid2_a4_r3 sid2_kv_r1 sid2_kv_r2
## 1% 53.37 219.95 209.30 128.65 123.51 63.65
## 5% 206.90 457.80 439.25 266.30 333.30 124.30
## 95% 56733.05 93996.75 24915.35 49737.55 70008.80 26199.65
## 99% 155619.58 340740.47 71256.45 179502.84 191287.62 70910.84
## sid2_kv_r3 sid2_mk_r1 sid2_mk_r2 sid2_mk_r3 tplr14_a2_r1 tplr14_a2_r2
## 1% 79.56 38.76 8.67 27.54 79.79 28.01
## 5% 181.65 149.40 39.70 88.65 265.60 144.50
## 95% 36439.95 44125.85 19466.60 22015.90 55944.05 26437.05
## 99% 173851.97 325579.26 80120.00 199122.10 156580.91 75442.43
## tplr14_a2_r3 tplr14_a4_r1 tplr14_a4_r2 tplr14_a4_r3 tplr14_kv_r1
## 1% 49.25 58.86 29.42 51.6 117.61
## 5% 159.20 256.60 108.00 206.7 279.20
## 95% 42558.05 32936.10 19007.45 43935.7 51560.20
## 99% 137846.52 124453.23 74196.67 163542.8 181462.32
## tplr14_kv_r2 tplr14_kv_r3 tplr14_mk_r1 tplr14_mk_r2 tplr14_mk_r3
## 1% 69.85 48.33 14.79 3.06 12.75
## 5% 190.85 106.50 167.20 27.20 70.30
## 95% 40841.55 31829.30 34915.20 13872.90 25991.40
## 99% 114441.28 112382.64 276270.40 76213.89 176416.70
## wt_a2_r1 wt_a2_r2 wt_a2_r3 wt_a4_r1 wt_a4_r2 wt_a4_r3 wt_kv_r1
## 1% 117.22 58.47 107.13 118.45 163.37 132.2 105.56
## 5% 347.10 189.40 321.75 343.85 301.20 400.1 239.30
## 95% 68184.00 33684.90 79445.90 72291.80 35956.25 64972.5 71698.90
## 99% 289922.49 144295.74 257789.31 337102.91 175661.17 229800.5 268936.49
## wt_kv_r2 wt_kv_r3 wt_mk_r1 wt_mk_r2 wt_mk_r3 wt_un_r1 wt_un_r2
## 1% 353.69 128.0 62.22 7.14 16.83 24.99 9.69
## 5% 693.50 371.2 188.65 48.10 93.55 111.00 86.90
## 95% 143775.20 62650.2 17918.70 9963.85 19159.60 53308.75 37782.40
## 99% 556533.90 201247.9 188269.80 31638.09 149831.81 216565.37 188065.44
## wt_un_r3
## 1% 29.58
## 5% 98.30
## 95% 40999.60
## 99% 154483.15
##
## Read depths:
## sample off_target on_target
## 1 eds1_a2_r1 10293.6923 131027.0769
## 2 eds1_a2_r2 16667.9615 86863.8654
## 3 eds1_a2_r3 11549.5000 81807.0385
## 4 eds1_a4_r1 12880.0000 56721.5192
## 5 eds1_a4_r2 20127.1538 77579.0962
## 6 eds1_a4_r3 13810.2115 66552.6538
## 7 eds1_kv_r1 8968.2885 48540.9038
## 8 eds1_kv_r2 13720.4423 57653.9423
## 9 eds1_kv_r3 7591.7885 43118.1538
## 10 eds1_mk_r1 6624.7308 24891.6923
## 11 eds1_mk_r2 12568.9615 25202.2308
## 12 eds1_mk_r3 8010.1923 19780.8846
## 13 gdna_r1 257.7308 130.0000
## 14 gdna_r2 222.5962 117.9038
## 15 gdna_r3 221.0962 107.0577
## 16 gh_a2_r1 9047.4808 56106.8269
## 17 gh_a2_r2 5719.6538 28665.5769
## 18 gh_a2_r3 11745.4038 62903.9038
## 19 gh_a4_r1 12718.0000 77494.9423
## 20 gh_a4_r2 3231.0000 16103.2885
## 21 gh_a4_r3 12728.1346 63452.2692
## 22 gh_kv_r1 16439.5577 87423.0577
## 23 gh_kv_r2 10894.2500 66312.0962
## 24 gh_kv_r3 9934.7500 55095.5000
## 25 gh_mk_r1 28393.0385 74583.2692
## 26 gh_mk_r2 10048.6154 19579.5577
## 27 gh_mk_r3 9101.2115 35561.0192
## 28 myc234_a2_r1 17147.2885 173448.3077
## 29 myc234_a2_r2 20013.8077 137079.2500
## 30 myc234_a2_r3 13162.0769 118163.8462
## 31 myc234_a4_r1 14583.7308 141427.0000
## 32 myc234_a4_r2 15988.9038 139505.6923
## 33 myc234_a4_r3 12642.5577 141270.3269
## 34 myc234_kv_r1 16805.7308 153357.1346
## 35 myc234_kv_r2 13056.3077 78432.8846
## 36 myc234_kv_r3 10231.9423 104777.3269
## 37 myc234_mk_r1 7113.0577 29603.9038
## 38 myc234_mk_r2 10610.2115 31244.2308
## 39 myc234_mk_r3 8029.7308 29322.6731
## 40 peds_a2_r1 11382.2115 63867.1346
## 41 peds_a2_r2 4808.6346 19919.7500
## 42 peds_a2_r3 12983.9231 89179.0577
## 43 peds_a4_r1 10727.0577 60789.4423
## 44 peds_a4_r2 251.3654 1810.6538
## 45 peds_a4_r3 17393.1923 180997.2692
## 46 peds_kv_r1 7259.8846 23109.1731
## 47 peds_kv_r2 4129.9423 13662.4808
## 48 peds_kv_r3 12285.5000 109193.4808
## 49 peds_mk_r1 10459.5577 13520.0385
## 50 peds_mk_r2 2020.2115 2364.3077
## 51 peds_mk_r3 19441.0000 102800.7308
## 52 r1ab_a2_r1 14232.5769 172116.8846
## 53 r1ab_a2_r2 11884.3077 98384.4038
## 54 r1ab_a2_r3 12880.7692 113736.3846
## 55 r1ab_a4_r1 18210.8269 253956.5385
## 56 r1ab_a4_r2 5823.5962 50585.0962
## 57 r1ab_a4_r3 11955.4231 117711.2692
## 58 r1ab_kv_r1 33187.4615 455334.0769
## 59 r1ab_kv_r2 7085.8846 65300.4615
## 60 r1ab_kv_r3 12562.4615 140331.1154
## 61 r1ab_mk_r1 33862.5577 316324.6154
## 62 r1ab_mk_r2 4431.5962 10869.5577
## 63 r1ab_mk_r3 8784.6346 57622.9038
## 64 sid2_a2_r1 19747.9808 134853.7692
## 65 sid2_a2_r2 2867.1154 17495.2308
## 66 sid2_a2_r3 10794.1538 72270.4423
## 67 sid2_a4_r1 25367.1154 238554.6154
## 68 sid2_a4_r2 7462.9038 64687.4231
## 69 sid2_a4_r3 11804.9038 97133.2308
## 70 sid2_kv_r1 14262.4231 92824.7308
## 71 sid2_kv_r2 5208.6154 30489.1154
## 72 sid2_kv_r3 10301.2308 61809.6346
## 73 sid2_mk_r1 17256.4231 62399.3846
## 74 sid2_mk_r2 4646.5577 10330.1731
## 75 sid2_mk_r3 9905.2500 55177.3654
## 76 tplr14_a2_r1 11102.6538 97662.8846
## 77 tplr14_a2_r2 5256.6346 40421.8269
## 78 tplr14_a2_r3 8794.7692 67227.5577
## 79 tplr14_a4_r1 8240.9231 91425.6923
## 80 tplr14_a4_r2 4817.2692 48701.1538
## 81 tplr14_a4_r3 10202.4808 90430.2500
## 82 tplr14_kv_r1 12141.4808 107228.0577
## 83 tplr14_kv_r2 7879.1731 57791.5962
## 84 tplr14_kv_r3 6953.3269 52472.3462
## 85 tplr14_mk_r1 14313.5577 61396.9038
## 86 tplr14_mk_r2 4135.9231 9047.4231
## 87 tplr14_mk_r3 8942.6731 33548.9038
## 88 wt_a2_r1 18653.1923 201090.2115
## 89 wt_a2_r2 8971.4615 73472.5577
## 90 wt_a2_r3 16988.8462 150178.7115
## 91 wt_a4_r1 20806.9808 279813.4808
## 92 wt_a4_r2 11855.0385 127103.8846
## 93 wt_a4_r3 15448.9615 182871.5192
## 94 wt_kv_r1 16784.3077 175559.7500
## 95 wt_kv_r2 35848.3462 289537.9231
## 96 wt_kv_r3 14108.0962 164451.6731
## 97 wt_mk_r1 10042.3846 92829.9038
## 98 wt_mk_r2 2209.6538 9139.4615
## 99 wt_mk_r3 7806.0577 61724.2692
## 100 wt_un_r1 12716.4038 20202.5192
## 101 wt_un_r2 10443.6923 17852.1923
## 102 wt_un_r3 9119.3846 16402.0577Coverage_summary
It shows warning message because we mistook to treat AT4G28410 → AT4G28420
coverage_summary(data)## Warning: Expected 4 pieces. Missing pieces filled with `NA` in 10608
## rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
## 20, ...].## Warning in as.data.frame.atacr(data): 強制変換により NA が生成されました## Picking joint bandwidth of 0.391## Picking joint bandwidth of 0.251
Correlation between replicate plot
sample_correlation_plot(data)
detect control genes based on Goodness of fit
auto_controls <- find_controls_by_GoF(data, which = "bait_windows")
auto_controls## [1] "AT1G01680_PUB54" "AT1G07160_AP2C2" "AT1G07920"
## [4] "AT1G19250_FMO1" "AT1G32640_MYC2" "AT1G43910"
## [7] "AT1G51920" "AT1G53625" "AT1G73805_SARD1"
## [10] "AT1G77120_ADH1" "AT2G04450_NUDT6" "AT2G17740_VLG"
## [13] "AT2G45760_BAP2" "AT3G13100_MRP7" "AT3G13610_F6'H1"
## [16] "AT3G26830_PAD3" "AT3G52430_PAD4" "AT4G05320_UBQ10"
## [19] "AT4G18960_AG" "AT4G21840_MSRB8" "AT4G39030_EDS5"
## [22] "AT5G26690_HIPP02" "AT5G39670_CML46" "AT5G42380_CML37"
## [25] "AT5G44420_PDF1.2A" "AT5G55450_nsLTP4.4"Control genes (Not ERGs)
pre_hoc_controls <- strsplit("AT1G07160_AP2C2,AT1G07920,AT1G32640_MYC2,AT1G51920,AT1G59860_HSP17.6A-CI,AT1G77120_ADH1,AT2G17740_VLG,AT2G19190_FRK1,AT3G27850_RPL12-C,AT3G45140_LOX2,AT4G01250_WRKY22,AT4G05320_UBQ10,AT4G18960_AG,AT4G28410_RSA1,AT5G03840_TFL1,AT5G09810_ACT7,AT5G44420_PDF1.2A", ",")[[1]]
pre_hoc_controls## [1] "AT1G07160_AP2C2" "AT1G07920"
## [3] "AT1G32640_MYC2" "AT1G51920"
## [5] "AT1G59860_HSP17.6A-CI" "AT1G77120_ADH1"
## [7] "AT2G17740_VLG" "AT2G19190_FRK1"
## [9] "AT3G27850_RPL12-C" "AT3G45140_LOX2"
## [11] "AT4G01250_WRKY22" "AT4G05320_UBQ10"
## [13] "AT4G18960_AG" "AT4G28410_RSA1"
## [15] "AT5G03840_TFL1" "AT5G09810_ACT7"
## [17] "AT5G44420_PDF1.2A"Control genes (Not ERGs & detected by GoF)
intersect(auto_controls, pre_hoc_controls)## [1] "AT1G07160_AP2C2" "AT1G07920" "AT1G32640_MYC2"
## [4] "AT1G51920" "AT1G77120_ADH1" "AT2G17740_VLG"
## [7] "AT4G05320_UBQ10" "AT4G18960_AG" "AT5G44420_PDF1.2A"Plot Goodness of fit
plot_GoF(data, controls = auto_controls)
plot_GoF(data, controls = pre_hoc_controls)
Normalization
First normalization … based on GoF.
norm_factors <- get_GoF_factors(data)
data$normalised_data <- scale_factor_normalise(data, scaling_factors = norm_factors)
plot_counts(data, which = "normalised_data")## Picking joint bandwidth of 0.393
Second normalization … basde on length of genes.
data$normalised_data <- normalise_by_window_width(data, which = "normalised_data")
plot_counts(data, which = "normalised_data")## Picking joint bandwidth of 0.344
### Save normalized read counts
write.csv(assays(data$normalised_data)[[1]], file="../outputs/rna_seq_normalized_count_in_mRNA.csv")Estimate BayesFactor and mark significantly different genes for many experiments
(from here, we remove genomic DNA dataset)
diff_expression <- estimate_bayes_factor_multiclass(data, "wt_un", which = "normalised_data", factor = 1.5)## t is large; approximation invoked.
## t is large; approximation invoked.## Warning: Column `window` joining character vector and factor, coercing into
## character vector## t is large; approximation invoked.## Warning: Column `window` joining character vector and factor, coercing into
## character vector## t is large; approximation invoked.
## t is large; approximation invoked.## Warning: Column `window` joining character vector and factor, coercing into
## character vector
## Warning: Column `window` joining character vector and factor, coercing into
## character vector## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.## Warning: Column `window` joining character vector and factor, coercing into
## character vector## t is large; approximation invoked.
## t is large; approximation invoked.## Warning: Column `window` joining character vector and factor, coercing into
## character vector## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.## Warning: Column `window` joining character vector and factor, coercing into
## character vector
## Warning: Column `window` joining character vector and factor, coercing into
## character vector
## Warning: Column `window` joining character vector and factor, coercing into
## character vector## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.## Warning: Column `window` joining character vector and factor, coercing into
## character vector## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.## Warning: Column `window` joining character vector and factor, coercing into
## character vector## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.## Warning: Column `window` joining character vector and factor, coercing into
## character vector## t is large; approximation invoked.## Warning: Column `window` joining character vector and factor, coercing into
## character vector## t is large; approximation invoked.
## t is large; approximation invoked.## Warning: Column `window` joining character vector and factor, coercing into
## character vector## t is large; approximation invoked.## Warning: Column `window` joining character vector and factor, coercing into
## character vector
## Warning: Column `window` joining character vector and factor, coercing into
## character vector
## Warning: Column `window` joining character vector and factor, coercing into
## character vector## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.## Warning: Column `window` joining character vector and factor, coercing into
## character vector## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.## Warning: Column `window` joining character vector and factor, coercing into
## character vector## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.## Warning: Column `window` joining character vector and factor, coercing into
## character vector
## Warning: Column `window` joining character vector and factor, coercing into
## character vector## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.## Warning: Column `window` joining character vector and factor, coercing into
## character vector## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.## Warning: Column `window` joining character vector and factor, coercing into
## character vector## t is large; approximation invoked.
## t is large; approximation invoked.## Warning: Column `window` joining character vector and factor, coercing into
## character vector
## Warning: Column `window` joining character vector and factor, coercing into
## character vector## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.## Warning: Column `window` joining character vector and factor, coercing into
## character vector## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.## Warning: Column `window` joining character vector and factor, coercing into
## character vector## t is large; approximation invoked.
## t is large; approximation invoked.## Warning: Column `window` joining character vector and factor, coercing into
## character vector
## Warning: Column `window` joining character vector and factor, coercing into
## character vector## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.## Warning: Column `window` joining character vector and factor, coercing into
## character vector## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.
## t is large; approximation invoked.## Warning: Column `window` joining character vector and factor, coercing into
## character vector## t is large; approximation invoked.## Warning: Column `window` joining character vector and factor, coercing into
## character vector
## Warning: Column `window` joining character vector and factor, coercing into
## character vectordiff_expression <- diff_expression[diff_expression$a != "gdna", ]
diff_expression %>%
dplyr::filter(is_sig == TRUE) %>%
dplyr::distinct(a)## a
## 1 eds1_a2
## 2 eds1_a4
## 3 eds1_kv
## 4 eds1_mk
## 5 gh_a2
## 6 gh_a4
## 7 gh_kv
## 8 gh_mk
## 9 myc234_a2
## 10 myc234_a4
## 11 myc234_kv
## 12 myc234_mk
## 13 peds_a2
## 14 peds_a4
## 15 peds_kv
## 16 peds_mk
## 17 r1ab_a2
## 18 r1ab_a4
## 19 r1ab_kv
## 20 r1ab_mk
## 21 sid2_a2
## 22 sid2_a4
## 23 sid2_kv
## 24 sid2_mk
## 25 tplr14_a2
## 26 tplr14_a4
## 27 tplr14_kv
## 28 tplr14_mk
## 29 wt_a2
## 30 wt_a4
## 31 wt_kv
## 32 wt_mkSave differential expression result
write.csv(diff_expression, file = "../outputs/diff_expression_RNA_seq.csv", row.names=FALSE)UpSetR (remove?)
make_UpSetR <- function(df) {
log2_fc <- direction <- a <- NULL
r <- df %>%
dplyr::mutate(
direction = ifelse(log2_fold_change > 0, "up", "down"),
category = paste0(direction, "_", a)
)
r <- r %>% split(r$category) %>%
lapply(function(x)
as.vector(dplyr::select(x, window)$window))
return(r)
}
diff_expression %>%
dplyr::filter(is_sig == TRUE) %>%
make_UpSetR() %>%
fromList() %>%
upset(
nsets = 56,
nintersects = NA,
order.by = c("degree", "freq"),
main.bar.color = "steelblue",
sets.bar.color = "aquamarine",
text.scale = 2,
line.size = 0,
mb.ratio = c(0.3, 0.7)
)
Heatmap of differential expression analysis
We remove AT4G28410 in this process.
All genes & all treatments
# make color list for each genes
ID_color_list <- read.csv("../data/ID_color_list.csv")
ID_color_list["color"] <- lapply(ID_color_list["color"], gsub, pattern="blue", replacement=rgb(86/255, 180/255, 233/255))
ID_color_list["color"] <- lapply(ID_color_list["color"], gsub, pattern="red", replacement=rgb(230/255, 159/255, 0))
rownames(ID_color_list) <- ID_color_list$ID
# make heatmap contains 51 genes and all mutants/treatment.
log2_matrix <- diff_expression %>%
reshape2::acast( window ~ a, value.var = "log2_fold_change")
log2_matrix[log2_matrix == -Inf] <- 0
log2_matrix<-na.omit(log2_matrix)
new_color_list <- ID_color_list$color[-41]
heatmap <- heatmap3::heatmap3(log2_matrix, cexRow=0.75, margin=c(12, 12), RowAxisColors=1, RowSideColors=new_color_list)
Save log matrix values
log2_matrix <- diff_expression %>%
reshape2::acast( window ~ a, value.var = "log2_fold_change")
write.csv(log2_matrix, file="../outputs/log_matrix_diff_expression.csv")Print gene ids in each cluster.
# cluster1
print(rownames(log2_matrix)[heatmap$rowInd][26:51])## [1] "AT3G52430_PAD4" "AT1G73805_SARD1" "AT2G04450_NUDT6"
## [4] "AT1G43910" "AT1G74710_ICS1" "AT2G14610_PR1"
## [7] "AT2G26400_ARD3" "AT4G11370_RHA1A" "AT2G30770_CYP71A13"
## [10] "AT2G04430_NUDT5" "AT1G65483" "AT5G44420_PDF1.2A"
## [13] "AT1G68200_CDM1" "AT3G13100_MRP7" "AT1G01680_PUB54"
## [16] "AT3G57950" "AT1G19250_FMO1" "AT2G45760_BAP2"
## [19] "AT3G10815" "AT3G13433" "AT5G55450_nsLTP4.4"
## [22] "AT4G28410_RSA1" "AT5G09810_ACT7" "AT1G53620"
## [25] "AT4G21840_MSRB8" "AT3G13610_F6'H1"# cluster2
print(rownames(log2_matrix)[heatmap$rowInd][14:25])## [1] "AT4G12720_NUDT7" "AT5G26690_HIPP02"
## [3] "AT1G59860_HSP17.6A-CI" "AT2G19190_FRK1"
## [5] "AT4G01250_WRKY22" "AT1G07160_AP2C2"
## [7] "AT1G35210" "AT5G39670_CML46"
## [9] "AT3G26830_PAD3" "AT1G53625"
## [11] "AT1G51920" "AT2G17740_VLG"# cluster3
print(rownames(log2_matrix)[heatmap$rowInd][1:13])## [1] "AT3G27850_RPL12-C" "AT4G18960_AG" "AT5G03840_TFL1"
## [4] "AT1G07920" "AT1G77120_ADH1" "AT4G05320_UBQ10"
## [7] "AT3G45140_LOX2" "AT1G32640_MYC2" "AT4G39030_EDS5"
## [10] "AT5G42380_CML37" "AT5G52730_HIPP11 " "AT5G13320_PBS3"
## [13] "AT2G24850_TAT3"each 1 treatment vs wt¶
all 51 genes, (vs “eds1”, “gh”, “peds”, “r1ab”)
log2_matrix <- diff_expression %>%
reshape2::acast( window ~ a, value.var = "log2_fold_change")
log2_matrix[log2_matrix == -Inf] <- 0
treatments <- c("eds1", "gh", "peds", "r1ab")
for (treat in treatments) {
pickup <- c(paste(treat, "_a2", sep=""),paste(treat, "_a4", sep=""),paste(treat, "_kv", sep=""),"wt_a2","wt_a4","wt_kv")
pickup_log2_matrix <- log2_matrix[,pickup]
pickup_log2_matrix<-na.omit(pickup_log2_matrix)
new_color_list <- ID_color_list$color[-41]
heatmap <- heatmap3::heatmap3(pickup_log2_matrix, cexRow=0.75, margin=c(12, 12), RowAxisColors=1, RowSideColors=new_color_list)
}
Pick up specific genes or pick up specific treatment to be clear (vs “myc234”, “sid2”, “tplr14”)
vs myc234
pick up Top 10 genes (myc234_a4 vs wt_a4)
treat <- "myc234"
log2_matrix <- diff_expression %>%
reshape2::acast( window ~ a, value.var = "log2_fold_change")
treat_vs_wt_a4 <- sort(log2_matrix[,paste(treat, "_a4", sep="")] / log2_matrix[,"wt_a4"])
pickup <- c(paste(treat, "_a2", sep=""),paste(treat, "_a4", sep=""),paste(treat, "_kv", sep=""),"wt_a2","wt_a4","wt_kv")
pickup_log2_matrix <- log2_matrix[,pickup]
pickup_log2_matrix <- pickup_log2_matrix[names(treat_vs_wt_a4)[c(1:10,42:51)],]
pickup_log2_matrix<-na.omit(pickup_log2_matrix)
new_color_list <- ID_color_list[names(treat_vs_wt_a4)[c(1:10,42:51)], "color"]
heatmap <- heatmap3::heatmap3(pickup_log2_matrix, cexRow=0.75, margin=c(12, 12), RowAxisColors=1, RowSideColors=new_color_list)
vs sid2
pick up Top 12 genes (sid2_a4 vs wt_a4)
treat <- "sid2"
log2_matrix <- diff_expression %>%
reshape2::acast( window ~ a, value.var = "log2_fold_change")
treat_vs_wt_a4 <- sort(log2_matrix[,paste(treat, "_a4", sep="")] / log2_matrix[,"wt_a4"])
pickup <- c(paste(treat, "_a2", sep=""),paste(treat, "_a4", sep=""),paste(treat, "_kv", sep=""),"wt_a2","wt_a4","wt_kv")
pickup_log2_matrix <- log2_matrix[,pickup]
pickup_log2_matrix <- pickup_log2_matrix[names(treat_vs_wt_a4)[c(1:12,40:51)],]
pickup_log2_matrix<-na.omit(pickup_log2_matrix)
new_color_list <- ID_color_list[names(treat_vs_wt_a4)[c(1:12,40:51)], "color"]
heatmap <- heatmap3::heatmap3(pickup_log2_matrix, cexRow=0.75, margin=c(12, 12), RowAxisColors=1, RowSideColors=new_color_list)
vs tplr14 series
pick up only kv, a4
treat <- "tplr14"
log2_matrix <- diff_expression %>%
reshape2::acast( window ~ a, value.var = "log2_fold_change")
treat_vs_wt_a4 <- sort(log2_matrix[,paste(treat, "_a4", sep="")] / log2_matrix[,"wt_a4"])
pickup <- c(paste(treat, "_a4", sep=""),paste(treat, "_kv", sep=""),"wt_kv","wt_a4")
pickup_log2_matrix <- log2_matrix[,pickup]
pickup_log2_matrix <- pickup_log2_matrix[names(treat_vs_wt_a4),]
pickup_log2_matrix<-na.omit(pickup_log2_matrix)
new_color_list <- ID_color_list[-41, "color"]
heatmap <- heatmap3::heatmap3(pickup_log2_matrix, cexRow=0.75, margin=c(12, 12), RowAxisColors=1, RowSideColors=new_color_list)
pick up only kv, a2
treat <- "tplr14"
log2_matrix <- diff_expression %>%
reshape2::acast( window ~ a, value.var = "log2_fold_change")
treat_vs_wt_a4 <- sort(log2_matrix[,paste(treat, "_a2", sep="")] / log2_matrix[,"wt_a2"])
pickup <- c(paste(treat, "_a2", sep=""),paste(treat, "_kv", sep=""),"wt_kv","wt_a2")
pickup_log2_matrix <- log2_matrix[,pickup]
pickup_log2_matrix <- pickup_log2_matrix[names(treat_vs_wt_a4),]
pickup_log2_matrix<-na.omit(pickup_log2_matrix)
new_color_list <- ID_color_list[-41, "color"]
heatmap <- heatmap3::heatmap3(pickup_log2_matrix, cexRow=0.75, margin=c(12, 12), RowAxisColors=1, RowSideColors=new_color_list)
pick up only kv, a4 & pick up top 20 genes
treat <- "tplr14"
log2_matrix <- diff_expression %>%
reshape2::acast( window ~ a, value.var = "log2_fold_change")
treat_vs_wt_a4 <- sort(log2_matrix[,paste(treat, "_a4", sep="")] / log2_matrix[,"wt_a4"])
pickup <- c(paste(treat, "_a4", sep=""),paste(treat, "_kv", sep=""),"wt_kv","wt_a4")
pickup_log2_matrix <- log2_matrix[,pickup]
pickup_log2_matrix <- pickup_log2_matrix[names(treat_vs_wt_a4)[c(1:20,32:51)],]
pickup_log2_matrix<-na.omit(pickup_log2_matrix)
new_color_list <- ID_color_list[names(treat_vs_wt_a4)[c(1:20,32:51)], "color"]
heatmap <- heatmap3::heatmap3(pickup_log2_matrix, cexRow=0.75, margin=c(12, 12), RowAxisColors=1, RowSideColors=new_color_list)
pick up only kv, a2 & pick up top 20 genes
treat <- "tplr14"
log2_matrix <- diff_expression %>%
reshape2::acast( window ~ a, value.var = "log2_fold_change")
treat_vs_wt_a4 <- sort(log2_matrix[,paste(treat, "_a2", sep="")] / log2_matrix[,"wt_a2"])
pickup <- c(paste(treat, "_a2", sep=""),paste(treat, "_kv", sep=""),"wt_kv","wt_a2")
pickup_log2_matrix <- log2_matrix[,pickup]
pickup_log2_matrix <- pickup_log2_matrix[names(treat_vs_wt_a4)[c(1:20,32:51)],]
pickup_log2_matrix<-na.omit(pickup_log2_matrix)
new_color_list <- ID_color_list[names(treat_vs_wt_a4)[c(1:20,32:51)], "color"]
heatmap <- heatmap3::heatmap3(pickup_log2_matrix, cexRow=0.75, margin=c(12, 12), RowAxisColors=1, RowSideColors=new_color_list)
check similarity between tplr14 and myc234
log2_matrix <- diff_expression %>%
reshape2::acast( window ~ a, value.var = "log2_fold_change")
log2_matrix[log2_matrix == -Inf] <- 0
treat1 <- "tplr14"
treat2 <- "myc234"
pickup <- c(paste(treat1, "_a2", sep=""),paste(treat1, "_a4", sep=""),paste(treat1, "_kv", sep=""),paste(treat2, "_a2", sep=""),paste(treat2, "_a4", sep=""),paste(treat2, "_kv", sep=""),"wt_a2","wt_a4","wt_kv")
pickup_log2_matrix <- log2_matrix[,pickup]
pickup_log2_matrix<-na.omit(pickup_log2_matrix)
new_color_list <- ID_color_list[-41, "color"]
heatmap <- heatmap3::heatmap3(pickup_log2_matrix, cexRow=0.75, margin=c(12, 12), RowAxisColors=1, RowSideColors=new_color_list)